Multiple Constraint Knapsack Problem

2018-06-21 08:14:49

If there is more than one constraint (for example, both a volume limit and a weight limit, where the volume and weight of each item are not related), we get the multiply-constrained knapsack problem, multi-dimensional knapsack problem, or m-dimensional knapsack problem.

How do I code this in the most optimized fashion? Well, one can develop a brute force recursive solution. May be branch and bound.. but essentially its exponential most of the time until you do some sort of memoization or use dynamic programming which again takes a huge amount of memory if not done well.

The problem I am facing is this

I have my knapsack function KnapSack( Capacity, Value, i) instead of the common KnapSack ( Capacity , i ) since I have upper limits on both of those. can anyone guide me with this? or provide suitable resources for solving these problems for reasonably large n

or is this NP complete ?

Thanks

Merge the constraints. Look at http://www.diku.dk/~pisinger/95-1.pdf chapter 1.3.1 called Merging the Constraints.

An example is say you have
variable , constraint1 , constraint2
1 , 43 , 66
2 , 65 , 54
3 , 34 , 49
4 , 99 , 32
5 , 2 , 88

Multiply the first constraint by some big number then add it to the second constraint.

So you have
variable , merged constraint
1 , 430066
2 , 650054
3 , 340049
4 , 990032
5 , 20088

From there do whatever algorithm you wanted to do with one constraint. The main limiter that comes to mind with this how many digits your variable can hold.

As a good example would serve the following problem:

Given an undirected graph G having positive weights and N vertices.

You start with having a sum of M money. For passing through a vertex i, you must pay S[i] money. If you don't have enough money - you can't pass through that vertex. Find the shortest path from vertex 1 to vertex N, respecting the above conditions; or state that such path doesn't exist. If there exist more than one path having the same length, then output the cheapest one. Restrictions: 1

Pseudocode:

Set states(i,j) as unvisited for all (i,j)
Set Min[i][j] to Infinity for all (i,j)

Min[0][M]=0

While(TRUE)

Among all unvisited states(i,j) find the one for which Min[i][j]
is the smallest. Let this state found be (k,l).

If there wasn't found any state (k,l) for which Min[k][l] is
less than Infinity - exit While loop.

Mark state(k,l) as visited

For All Neighbors p of Vertex k.
   If (l-S[p]>=0 AND
    Min[p][l-S[p]]>Min[k][l]+Dist[k][p])
      Then Min[p][l-S[p]]=Min[k][l]+Dist[k][p]
   i.e.
If for state(i,j) there are enough money left for
going to vertex p (l-S[p] represents the money that
will remain after passing to vertex p), and the
shortest path found for state(p,l-S[p]) is bigger
than [the shortest path found for
state(k,l)] + [distance from vertex k to vertex p)],
then set the shortest path for state(i,j) to be equal
to this sum.
End For

End While

Find the smallest number among Min[N-1][j] (for all j, 0<=j<=M);
if there are more than one such states, then take the one with greater
j. If there are no states(N-1,j) with value less than Infinity - then
such a path doesn't exist.

Knapsack with multiple constraints is a packing problem. Read up. http://en.wikipedia.org/wiki/Packing_problem

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